OpenCV - 在相似图像中找不到正确的轮廓

Question

the task I want to do looks pretty simple: I take as input several images with an object centered in the photo and a little color chart needed for other purposes. My code normally works for the majority of the cases, but sometimes fails miserably and I just can't understand why.

For example (these are the source images), it works correctly on this https://imgur.com/PHfIqcb but not on this https://imgur.com/qghzO3V

Here's the code of the interested part:

img = cv2.imread(path)
height, width, channel = img.shape
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

kernel = np.ones((31, 31), np.uint8)
dil = cv2.dilate(gray, kernel, iterations=1)
_, th = cv2.threshold(dil, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
th_er1 = cv2.bitwise_not(th)

_, contours, _= cv2.findContours(th_er1, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
areas = [cv2.contourArea(c) for c in contours]
max_index = np.argmax(areas)
cnt=contours[max_index]

x,y,w,h = cv2.boundingRect(cnt)

After that I'm just going to crop the image accordingly to the given results (getting the biggest rectangle contour), basically cutting off the photo only the main object.

But as I said, using very similar images sometimes works and sometimes not.

Thank you in advance.

Answer 1

也许您可以尝试不使用 otsu 的方法，而只需手动设置阈值，如果可能的话... ;)

Answer 2

You can use the Canny edge detector. In the two images, there is a good threshold value to isolate the object in the center of the image. After applying the threshold, we blur the results and apply the Canny edge detector before finding the contours:

import cv2
import numpy as np

def process(img):
    img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    _, thresh = cv2.threshold(img_gray, 190, 255, cv2.THRESH_BINARY_INV)
    img_blur = cv2.GaussianBlur(thresh, (3, 3), 1)
    img_canny = cv2.Canny(img_blur, 0, 0)
    kernel = np.ones((5, 5))
    img_dilate = cv2.dilate(img_canny, kernel, iterations=1)
    return cv2.erode(img_dilate, kernel, iterations=1)

def get_contours(img):
    contours, hierarchies = cv2.findContours(process(img), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
    cnt = max(contours, key=cv2.contourArea)
    cv2.drawContours(img, [cnt], -1, (0, 255, 0), 30)
    x, y, w, h = cv2.boundingRect(cnt)
    cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 255), 30)

img = cv2.imread("image.jpeg")
get_contours(img)
cv2.imshow("Result", img)
cv2.waitKey(0)

Input images:

Output images:

The green outlines are the contours of the objects, and the red outlines are the bounding boxes of the objects.